Dissertations / Theses: 'Querying (Computer science)'

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Yang, Lei. "Querying Graph Structured Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1410434109.

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Chavda, Manoj. "Visually querying object-oriented databases." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/17516.

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Bibliography: pages 141-145.
As database requirements increase, the ability to construct database queries efficiently becomes more important. The traditional means of querying a database is to write a textual query, such as writing in SQL to query a relational database. Visual query languages are an alternative means of querying a database; a visual query language can embody powerful query abstraction and user feedback techniques, thereby making them potentially easier to use. In this thesis, we develop a visual query system for ODMG-compliant object-oriented databases, called QUIVER. QUIVER has a comprehensive expressive power; apart from supporting data types such as sets, bags, arrays, lists, tuples, objects and relationships, it supports aggregate functions, methods and sub-queries. The language is also consistent, as constructs with similar functionality have similar visual representations. QUIVER uses the DOT layout engine to automatically layout a query; QUIVER queries are easily constructed, as the system does not constrain the spatial arrangement of query items. QUIVER also supports a query library, allowing queries to be saved, retrieved and shared among users. A substantial part of the design has been implemented using the ODMG-compliant database system O₂, and the usability of the interface as well as the query language itself is presented. Visual queries are translated to OQL, the standard query language proposed by the ODMG, and query answers are presented using O₂ Look. During the course of our investigation, we conducted a user evaluation to compare QUIVER and OQL. The results were extremely encouraging in favour of QUIVER.

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Wu, Chao-Hui. "Querying and synchronizing multimedia presentations /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488186329501598.

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Hossain, Md Delwar. "Querying communities of interest in peer database networks." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26926.

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Peer-to-Peer (P2P) networks are used primarily for file sharing. P2P user communities are continuing to grow rapidly, but there is no specific query mechanism for communities of interest in peer database networks. Peer databases are linked to each other through acquaintances. These are individual, independently developed databases that contain local data. Many researchers have tackled the problem of query processing in P2P networks. Moreover, researchers have started to investigate community-based querying in peer database networks. The focus of this thesis is to study a query translation mechanism, to develop an algorithm for querying communities of interest in peer database networks, and to implement a prototype of this algorithm. P2P communities are formed using common claimed items. First, we investigate an existing community formation and discovery algorithm for a text file, as well as an existing query translation mechanism for peer databases. After that, we develop a community-based search algorithm for peer databases. The developed algorithm combines the aforementioned community formation and discovery with the query translation mechanisms. A prototype has been developed and experimental results are shown. An approach for discovering communities 'on the fly' is introduced and a method for optimizing the community discovery technique is shown.

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Chiu, Tsz Wai. "WinyDB : collaboratively querying sensor networks through handheld devices /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20CHIUT.

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Colgrove, Matthew Edward. "Querying Geographically Dispersed, Heterogeneous Data Stores: The PPerfXchange Approach." PDXScholar, 2002. https://pdxscholar.library.pdx.edu/open_access_etds/2665.

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This thesis details PPerfXchange’s approach for querying geographically dispersed heterogeneous data stores. While elements of PPerfXchange’s method have been implemented for other application areas, PPerfXchange shows how these elements can be applied to parallel performance analysis. The accomplishments of this thesis are: The design of an architecture for PPerfXchange, giving a uniform method to query heterogeneous data stores; A proof of concept prototype implementation of PPerfXchange including a partial implementation of an XQuery processor and a relational database virtual XML document; and Evaluation of PPerfXchange using example parallel performance analysis data.

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Qiao, Shi. "QUERYING GRAPH STRUCTURED RDF DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1447198654.

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Sun, Yi. "Querying with Ontological Terminologies And their Annotations." Miami University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=miami1177653662.

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Lam, Wai-Yeung. "XCQ : a framework for XML compression and querying /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20LAM.

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Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 142-147). Also available in electronic version. Access restricted to campus users.

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Li, Jing, and 李晶. "Advanced spatial queries with textual and social components." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49617837.

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The emerging new services for GPS and mobile users have developed applications that access and exploit spatial objects with new components (e.g. text and social network). Web objects, including blogs, tweets, photos and videos, are embedded into a map by the APIs of map service providers, where textual messages are associated with geographic information. Location-based social networking services, arising from Facebook and Foursquare, allow users to browse and share their traces of locations among the social networks. Among this class of applications, the highlight is that the retrieved spatial objects (e.g. points of interest and moving users) are featured with new components. Integrating such new components into the spatial query processing has produced large amounts of promising results. However, handling new components along with the retrieval of spatial objects increases the complexity of such joint query processing significantly. Thus, management over data from such multiple domains has been received considerable attention from database research community. In this thesis, we introduce three interesting problems and study their sophisticated solutions for processing spatial objects with new components: (i) category-aware optimal route query (CORQ), (ii) social and spatial ranking query (SSRQ), and (iii) efficient notification of meeting point (ENMP) query. Our results for (i) and (ii) facilitate the retrieval of spatial objects from multiple domains while our solutions for (iii) provide effective tools for synchronous management of multiple moving users from a social network. Category-aware optimal route queries (CORQ) are generalized from the traveling salesman problem and enable users to retrieve shortest routes covering selected categories. Social and spatial ranking queries (SSRQ) are relevant to spatial object recommendations using social information and allow users to obtain the spatial objects that not only are near their locations but also impress them with high social influence. Efficient notification of meeting point (ENMP) queries are variants of aggregate nearest neighbor queries and provide real-time rearrangement for multiple moving users according to their locations. Query processing in such multiple domains is complicated due to the mixture of domain information and their integration within one search. Naïve algorithms for these problems incur either numerous expensive evaluations or massive communication cost, which render them inapplicable to large datasets. Our main research purpose is to design efficient and effective solutions for the proposed problems, that avoid the aforementioned shortcomings of naïve algorithms.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy

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Jain, Prateek. "Linked Open Data Alignment & Querying." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1345575500.

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Wang, Song. "State-Slice: A New Stream Query Optimization Paradigm for Multi-query and Distributed Processing." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-032508-044505/.

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Sun, Christina M. Eng Massachusetts Institute of Technology. "A natural language interface for querying graph databases." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119708.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-69).
An increasing amount of knowledge in the world is stored in graph databases. However, most people have limited or no understanding of database schemes and query languages. Providing a tool that translates natural language queries into structured queries allows people without this technical knowledge or specific domain expertise to retrieve information that was previously inaccessible. Many existing natural language interfaces to databases (NLIDB) propose solutions that may not generalize well to multiple domains and may require excessive feature engineering, manual customization, or large amounts of annotated training data. We present a method for constructing subgraph queries which can represent a graph of activities, events, persons, behaviors, and relations, for search against a graph database containing information from a variety of data sources. Our model interprets complex natural language queries by using a pipeline of named entity recognition and binary relation extraction models to identify key entities and relations corresponding to graph components such as nodes, attributes, and edges. This information is combined in order to create structured graph queries, which may then be applied to graph databases. By breaking down the translation task into a pipeline of several submodules, our model achieves a prediction accuracy of 46.9 % with a small training set of only 218 sentences.
by Christina Sun.
M. Eng.

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Baxter, Jay. "BayesDB : querying the probable implications of tabular data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91451.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
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Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-95).
BayesDB, a Bayesian database table, lets users query the probable implications of their tabular data as easily as an SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with little statistics knowledge can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries. BayesDB is suitable for analyzing complex, heterogeneous data tables with no preprocessing or parameter adjustment required. This generality rests on the model independence provided by BQL, analogous to the physical data independence provided by the relational model. SQL enables data filtering and aggregation tasks to be described independently of the physical layout of data in memory and on disk. Non-experts rely on generic indexing strategies for good-enough performance, while experts customize schemes and indices for performance-sensitive applications. Analogously, BQL enables analysis tasks to be described independently of the models used to solve them. Non-statisticians can rely on a general-purpose modeling method called CrossCat to build models that are good enough for a broad class of applications, while experts can customize the schemes and models when needed. This thesis defines BQL, describes an implementation of BayesDB, quantitatively characterizes its scalability and performance, and illustrates its efficacy on real-world data analysis problems in the areas of healthcare economics, statistical survey data analysis, web analytics, and predictive policing.
by Jay Baxter.
M. Eng.

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Devadithya, Tharaka. "A graph based cache system for efficient querying in distributed triplestores." [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3324608.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2008.
Title from PDF t.p. (viewed on May 14, 2009). Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4837. Adviser: Randall Bramley.

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Zhang, Shijie. "Index-based Graph Querying and Matching in Large Graphs." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1263256028.

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Thesis(Ph.D.)--Case Western Reserve University, 2010
Title from PDF (viewed on 2010-04-12) Department of Electrical Engineering and Computer Science (EECS) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Li, Ruirui, and 李锐瑞. "A probabilistic approach to diversified query recommendation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49799757.

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The effectiveness of keyword-based search engines depends largely on the ability of a user to formulate proper queries that are both expressive and selective. However, web search queries issued by casual users are often short and with limited expressiveness. Query recommendation is a popular technique employed by search engines to help users refine their queries. Traditional similarity-based methods, however, often result in redundant and monotonic recommendations. We identify five basic requirements of a query recommendation system, namely relevancy, redundancy-free, diversity, ranking and efficiency. In particular, we focus on the requirements of redundancy-free and diversified recommendations. We propose the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected. Through query construction from the query concepts, we are able to avoid recommendation redundancy. (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification. Through a comprehensive user study we compare DQR against five other recommendation methods on real search log datasets. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations. At the same time, DQR also achieves high efficiency performance.
published_or_final_version
Computer Science
Master
Master of Philosophy

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Dinakar, Rohit. "Ontology based Querying and Integration of Heterogeneous Flat Files." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285063965.

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Morgan, Fabian F. 1981. "Querying over heterogeneous XML schemas in a content management system." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87283.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 51-52).
by Fabian F. Morgan.
M.Eng.

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Sabesan, Manivasakan. "Querying Data Providing Web Services." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-128928.

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Web services are often used for search computing where data is retrieved from servers providing information of different kinds. Such data providing web services return a set of objects for a given set of parameters without any side effects. There is need to enable general and scalable search capabilities of data from data providing web services, which is the topic of this Thesis. The Web Service MEDiator (WSMED) system automatically provides relational views of any data providing web service operations by reading the WSDL documents describing them. These views can be queried with SQL. Without any knowledge of the costs of executing specific web service operations the WSMED query processor automatically and adaptively finds an optimized parallel execution plan calling queried data providing web services. For scalable execution of queries to data providing web services, an algebra operator PAP adaptively parallelizes calls in execution plans to web service operations until no significant performance improvement is measured, based on monitoring the flow from web service operations without any cost knowledge or extensive memory usage. To comply with the Everything as a Service (XaaS) paradigm WSMED itself is implemented as a web service that provides web service operations to query and combine data from data providing web services. A web based demonstration of the WSMED web service provides general SQL queries to any data providing web service operations from a browser. WSMED assumes that all queried data sources are available as web services. To make any data providing system into a data providing web service WSMED includes a subsystem, the web service generator, which generates and deploys the web service operations to access a data source. The WSMED web service itself is generated by the web service generator.
eSSENCE

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Bandyopadhyay, Bortik. "Querying Structured Data via Informative Representations." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595447189545086.

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Li, Boyang. "Private data querying in the precomputation model." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1312292383.

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Lau, Ching Hin. "An I/O-efficient data structure for querying XML with inherited attributes /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20LAU.

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Wang, Xin. "Querying the web of data with low latency : high performance distributed SPARQL processing and benchmarking." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/368261/.

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The Web of Data extends the World Wide Web (WWW) in a way that applications can understand information and cooperate with humans on complex tasks. The basis of performing complex tasks is low latency queries over the Web of Data. The large scale and distributed nature of the Web of Data have negative impacts on several critical factors for efficient query processing, including fast data transmission between datasets, predictable data distribution and statistics that summarise and describe certain patterns in the data. Moreover, it is common on the Web of Data that the same resource is identified by multiple URIs. This phenomenon, named co-reference, potentially increases the complexity of query processing, and makes it even harder to obtain accurate statistics. With the aforementioned challenges, it is not clear whether it is possible to achieve efficient queries on the Web of Data on a large scale. In this thesis, we explore techniques to improve the efficiency of querying the Web of Data on a large scale. More specifically, we investigate two typical scenarios on the Web of Data, which are: 1) the scenario in which all datasets provide detailed statistics that are possibly available on a large scale, and 2) the scenario in which co-reference is taken into account, and datasets’ statistics are not reliable. For each scenario we explore existing and novel optimisation techniques that are tailored for querying the Web of Data, as well as well developed techniques with careful adjustments. For the scenario with detailed statistics we provide a scheme that implements a statistics query optimisation approach that requires detailed statistics, and intensively exploits parallelism. We propose an efficient algorithm called Parallel Sub-query Identification () to increase the degree of parallelism. () breaks a SPARQL query into sub-queries that can be processed in parallel while not increasing network traffic. We combine with dynamic programming to produce query plans with both minimum costs and a fair degree of parallelism. Furthermore, we develop a mechanism that maximally exploits bandwidth and computing power of datasets. For the scenario having co-reference and without reliable statistics we provide a scheme that implements a dynamic query optimisation approach that takes co-reference into account, and utilises runtime statistics to elevate query efficiency even further. We propose a model called Virtual Graph to transform a query and all its co-referent siblings into a single query with pre-defined bindings. Virtual Graph reduces the large number of outgoing and incoming requests that is required to process co-referent queries individually. Moreover, Virtual Graph enables query optimisers to find the optimal plan with respect to all co-referent queries as a whole. () is used in this scheme as well but provides a higher degree of parallelism with the help of runtime statistics. A Minimum-Spanning-Tree-based algorithm is used in this scheme as a result of using runtime statistics. The same parallel execution mechanism used in the previous scenario is adopted here as well. In order to examine the effectiveness of our schemes in practice, we deploy the above approaches in two distributed SPARQL engines, LHD-s and LHD-d respectively. Both engines are implemented using a popular Java-based platform for building Semantic Web applications. They can be used as either standalone applications or integrated into existing systems that require quick response of Linked Data queries. We also propose a scalable and flexible benchmark, called Distributed SPARQL Evaluation Framework (DSEF), for evaluating optimisation approaches in the Web of Data. DSEF adopts a expandable virtual-machine-based structure and provides a set of efficient tools to help easily set up RDF networks of arbitrary sizes. We further investigate the proportion and distribution of co-reference in the real world, based on which DESF is able to simulate co-reference for given RDF datasets. DSEF bases its soundness in the usage of widely accepted assessment data and queries. By comparing both LHD-s and LHD-d with existing approaches using DSEF, we provide evidence that neither existing statistics provided by datasets nor cost estimation methods, are sufficiently accurate. On the other hand, dynamic optimisation using runtime statistics together with carefully tuned parallelism are promising for significantly reducing the latency of large scale queries on the Web of Data. We also demonstrate that () and Virtual Graph algorithms significantly increase query efficiency for queries with or without co-reference. In summary, the contributions of this these include: 1) proposing two schemes for improving query efficiency in two typical scenarios in the Web of Data; 2) providing implementations, named LHD-s and LHD-d, for the two schemes respectively; 3) proposing a scalable and flexible evaluation framework for distributed SPARQL engines called DSEF; and 4) showing evidence that runtime-statistics-based dynamic optimisation with parallelism are promising to reduce latency of Linked Data queries on a large scale.

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Thiagarajan, Arvind. "Representing and querying regression models in a relational database management system." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42254.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 77-79).
Curve fitting is a widely employed, useful modeling tool in several financial, scientific, engineering and data mining applications, and in applications like sensor networks that need to tolerate missing or noisy data. These applications need to both fit functions to their data using regression, and pose relational-style queries over regression models. Unfortunately, existing DBMSs are ill suited for this task because they do not include support for creating, representing and querying functional data, short of brute-force discretization of functions into a collection of tuples. This thesis describes FunctionDB, a novel DBMS that extends the state of the art. FunctionDB treats functions output by regression as first-class citizens that can be queried declaratively and manipulated like traditional database relations. The key contributions of FunctionDB are a compact, algebraic representation for regression models as piecewise functions, and an algebraic query processor that executes declarative queries directly on this representation as combinations of algebraic operations like function inversion, zero finding and symbolic integration. FunctionDB is evaluated on two real world data sets: measurements from a temperature sensor network, and traffic traces from cars driving on Boston roads. The results show that operating in the functional domain has substantial accuracy advantages (over 15% for some queries) and order of magnitude (10x-100x) performance gains over existing approaches that represent models as discrete collections of points. The thesis also describes an algorithm to maintain regression models online, as new raw data is inserted into the system. The algorithm supports a sustained insertion rate of the order of a million records per second, while generating models no less compact than a clairvoyant (offline) strategy.
by Arvind Thiagarajan.
S.M.

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Breed, Aditi. "Querying semantically heterogeneous data sources using ontologies." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1089.

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Kwan, Kang-lun. "Adaptive stream filters for entity-based queries with non-value tolerance." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B3884333X.

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Kwan, Kang-lun, and 關庚麟. "Adaptive stream filters for entity-based queries with non-value tolerance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B3884333X.

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Zhang, Yinuo, and 张一诺. "Evaluating continuous probabilistic queries over constantly-evolving data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45589835.

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Chen, Lei. "Answering why-not questions on spatial keyword top-k queries /Chen Lei." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/365.

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With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. However, it is in some cases difficult for users to specify appropriate query parameters. After a user issues an initial query and gets back the result, the user may find that some expected objects are missing and may wonder why. Answering the resulting why-not questions can aid users in retrieving better results and thus improve the overall utility of the query functionality. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. In this dissertation, we take the first step to study the why-not questions on spatial keyword top-k queries. We provide techniques that allow different revisions of spatial keyword queries such that their results include one or more desired, but missing objects. Detailed problem analysis and extensive experimental studies consistently demonstrate the effectiveness and robustness of our proposed techniques in a broad range of settings.

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He, Bingsheng. "Cache-oblivious query processing /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20HE.

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Cheng, James Sheung-Chak. "Efficient query processing on graph databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20CHENG.

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Zhang, Shiming, and 张世明. "Scalable skyline evaluation in multidimensional and partially ordered domains." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46973916.

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Tew, Kevin. "Skuery : manipulation of S-expressions using Xquery techniques /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1677.pdf.

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Jin, Guang. "Towards Spatial Queries over Phenomena in Sensor Networks." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/JinG2009.pdf.

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Papadopoulos, Stavros. "Authenticated query processing /." View abstract or full-text, 2010. http://library.ust.hk/cgi/db/thesis.pl?CSED%202010%20PAPADO.

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Lin, Zhifeng, and 林志锋. "Advanced spatial queries in wireless ad hoc networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224295.

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Jin, Yifan, and 金一帆. "A filter-based protocol for continuous queries over imprecise locationdata." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49617953.

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In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries, e.g., \Give me the ID of a battalion which is the closest to a military base within the next hour." Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication and energy costs while maintaining a high query quality.
published_or_final_version
Computer Science
Master
Master of Philosophy

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Lin, Zhifeng. "Advanced spatial queries in wireless ad hoc networks." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224295.

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Lian, Xiang. "Efficient query processing over uncertain data /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20LIAN.

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Mayes, Stephen Frederick. "ADVANCED INTERFACE FOR QUERYING GRAPH DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196443381.

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Wang, Ling. "Updating XML views." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-082406-013940/.

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Fan, Zhe. "Secure subgraph query services." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/229.

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Graphs are powerful tools for a wide range of real applications, from Biological and Chemical Databases, Social Networks, Citation Networks to Knowledge Bases. Large graph data repositories have been consistently found in recent applications. Due to the high complexity of graph queries, e.g., NP-Completeness of subgraph query, and the lack of IT expertise, hosting efficient graph query services for the owners of graph data has been a technically challenging task. And hence, they may prefer to outsource their services to third-party service providers (SPs) for scalability, elasticity and efficiency. Unfortunately, SPs may not always be trusted. Security, typically the integrity and confidentiality, of the data, has been recognized as one of the critical attributes of Quality of Services (QoS). This directly influences the willingness of both data owners and query clients to use SP’s services. To address these concerns, this thesis proposes novel techniques to solve both authentication-aware and privacy-aware subgraph query. Firstly, we study authenticated subgraph query services (Chapter 3). To support the service, we propose Merkle IFTree (MIFTree) where Merkle hash trees are applied into our Intersection-aware Feature-subgraph Tree (IFTree). IFTree aims to minimize I/O in a well-received subgraph query paradigm namely the filtering-and-verification framework. The structures required to be introduced to verification objects (VOs) and the authentication time are minimized. Subsequently, the overall response time is minimized. For optimizations, we propose an enhanced authentication method on MIFTree. Secondly, we propose structure-preserving subgraph query services (Chapter 4). A crucial step of this part is to transform the seminal subgraph isomorphism algorithm (the Ullmann’s algorithm) into a series of matrix operations. We propose a novel cyclic group based encryption (CGBE) method for private matrix operations. We propose a protocol that involves the query client and static indexes for optimizations. We prove that the structural information of both query graph and data graph are preserved under CGBE and analyze the privacy preservation in the presence of the optimizations. Thirdly, we propose asymmetric structure-preserving subgraph query processing (Chapter 5), where the data graph is publicly known and the query structure/topology is kept secret. Unlike other previous methods for subgraph queries, this part proposes a series of novel optimizations that only exploit graph structures, not the queries. Further, we propose a robust query encoding and adopt our proposed cyclic group based encryption method, so that the query processing can be transformed into a series of private matrix operations and performed securely. The effectiveness and efficiency of all the techniques presented in this thesis are experimentally evaluated with both real-world and synthetic dataset

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Jiang, Jiaxin. "Efficient frameworks for keyword search on massive graphs." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/806.

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Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. However, these keyword search semantics and algorithms encounter efficiency or scalability issues. In this thesis, we propose new three generic frameworks or index techniques to address these issues. The thesis results show that the keyword search on massive graphs under different scenarios can be effective and efficient, which would facilitate keyword search services on graphs in the real world. First, we study the keyword search on massive knowledge graphs. In particular, we propose a generic ontology- based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. The novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure G. Regarding query evaluation, we transform a keyword search q into Q according to GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index's applicability, we show popular indexing techniques for keyword search can be easily implemented on top of BiG-index. Our extensive experiments show that BiG-index clearly reduced the runtimes of popular keyword search algorithms. Second, we study the problem of keyword search on public-private graph. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public-private networks has not yet been studied. Hence, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. We select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that span through them. Third, we study the keyword search in distributed graph evaluation systems. In the recent research on query evaluation, parallel evaluation has attracted much interest. However, the study on keyword search on distributed graphs has still been limited. We propose a novel distributed keyword search framework called DKWS. We propose a notify-push paradigm which can exchange the upper bounds of answers across all the workers asynchronously. In particular, the workers notify the coordinator when the local upper bound is refined. The coordinator pushes the refined global upper bound to all the workers. Moreover, we propose an efficient and generic keyword search algorithm for the workers. We have implemented DKWS on top of GRAPE, a distributed graph process system from our previous research collaboration. Extensive experimental results show that DKWS outperforms current-state-of-art techniques

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Acar, Aybar C. "Query consolidation interpreting queries sent to independent heterogenous databases /." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3223.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 169. Thesis director: Amihai Motro. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Title from PDF t.p. (viewed Aug. 27, 2008). Includes bibliographical references (p. 162-168). Also issued in print.

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El-Sayed, Maged F. "Incremental maintenance of materialized Xquery views." Link to electronic dissertation, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-082305-120333/.

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Ding, Luping. "Metadata-aware query processing over data streams." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-042208-194826/.

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Li, Jin. "Window Queries Over Data Streams." PDXScholar, 2008. https://pdxscholar.library.pdx.edu/open_access_etds/2675.

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Evaluating queries over data streams has become an appealing way to support various stream-processing applications. Window queries are commonly used in many stream applications. In a window query, certain query operators, especially blocking operators and stateful operators, appear in their windowed versions. Previous research work in evaluating window queries typically requires ordered streams and this order requirement limits the implementations of window operators and also carries performance penalties. This thesis presents efficient and flexible algorithms for evaluating window queries. We first present a new data model for streams, progressing streams, that separates stream progress from physical-arrival order. Then, we present our window semantic definitions for the most commonly used window operators—window aggregation and window join. Unlike previous research that often requires ordered streams when describing window semantics, our window semantic definitions do not rely on physical-stream arrival properties. Based on the window semantic definitions, we present new implementations of window aggregation and window join, WID and OA-Join. Compared to the existing implementations of stream query operators, our implementations do not require special stream-arrival properties, particularly stream order. In addition, for window aggregation, we present two other implementations extended from WID, Paned-WID and AdaptWID, to improve excution time by sharing sub-aggregates and to improve memory usage for input with data distribution skew, respectively. Leveraging our order-insenstive implementations of window operators, we present a new architecture for stream systems, OOP (Out-of- Order Processing). Instead of relying on ordered streams to indicate stream progress, OOP explicitly communicates stream progress to query operators, and thus is more flexible than the previous in-order processing (IOP) approach, which requires maintaining stream order. We implemented our order-insensitive window query operators and the OOP architecture in NiagaraST and Gigascope. Our performance study in both systems confirms the benefits of our window operator implementations and the OOP architecture compared to the commonly used approaches in terms of memory usage, execution time and latency.

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Wang, Fan. "SEEDEEP: A System for Exploring and Querying Deep Web Data Sources." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1279758181.

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Dong, Xin. "Providing best-effort services in dataspace systems /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6902.

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Dissertations / Theses on the topic 'Querying (Computer science)'

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